Annotated data is an integral part of many machine learning and artificial intelligence applications. At the same time, it is one of the most time-consuming and labor-intensive parts of ML projects. According to McKinsey, data annotation is one of the top limitations of AI implementation for organizations. We’ll explore what data annotation is and why it matters.

What is data annotation?

Data annotation is the process of labeling data in various formats such as video, images, or text so that machines can understand it. For supervised machine learning, labeled datasets are especially crucial because ML models need to understand input patterns to process them and produce accurate results.

Why does data annotation matter?

Annotated data is the lifeblood of supervised learning models since the performance and accuracy of such models depend on the quality and quantity of annotated data. These models train and learn from correctly annotated data and produce results for problems such as:

  • Classification: Assigning test data into specific categories. For instance, predicting whether a patient has a disease and assigning their health data to “disease” or “no disease” categories is a classification problem.
  • Regression: Establishing a relationship between dependent and independent variables. Estimating the relationship between the budget to advertising and sales of a product is an example of a regression problem.
Source: Diego Calvo

Some examples of why annotated data matters include:

  • Machine learning applications, such as chatbots powered by accurately annotated data can enable businesses to provide a better customer experience. According to Gartner, 70% of customer interactions will be converted to conversational AI applications such as chatbots and virtual assistants by 2022.
  • Training machine learning models of self-driving cars involve annotated video data. Individual objects in videos are annotated which allow machines to predict the movements of objects.

What are the different types of data annotation?

Different data annotation techniques can be used depending on the machine learning application. Some of the most common types are:

  1. Semantic annotation: Semantic annotation is the process of tagging text documents. By tagging documents with relevant concepts, it makes unstructured content easier to find. Computers can interpret and read the relationship between a specific part of metadata and a resource described by semantic annotation.
  2. Text annotation: It trains machines how to recognize human emotions through words. For example, chatbots can identify the user’s request with the keywords taught to the machine and offer solutions. If annotations are inaccurate, the machine is unlikely to provide a useful solution. Better text annotations provide a better customer experience. During the data annotation process, with text annotation, some specific keywords, sentences, etc. are assigned to data. This process is also known as training data for AI and ML models. Comprehensive text annotations are crucial for accurate machine training. Some types of text annotation are:
    • Intent Annotation: For example, the sentence “I want to chat with David” indicates a request. Intent annotation analyzes the needs behind such texts and categorizes them such as requests and approvals.
    • Sentiment Annotation: Sentiment annotation analyzes the emotions behind the texts. Machine learning models are trained with sentiment annotation text annotation type to find the true emotions behind the texts. For example, by reading the comments left by customers about the products, ML models understand the attitude and emotion behind the text and then make the relevant labeling such as positive, negative, or neutral.
  1. Image annotation: It is the process of labeling images to train an AI or ML model. For example, a machine learning model gains a high level of comprehension like a human with tagged digital images and can interpret the images it sees. With data annotation, objects in any image are labeled. Depending on the use case, the number of labels on the image may increase. There are four fundamental types of image annotation:
    • Image Classification: First, the machine trained with annotated images then determines what an image represents with the predefined annotated images.
    • Object Recognition/Detection: It is a further version of image classification. It is the correct description of the numbers and exact positions of entities in the image. While a label is assigned to the entire image in image classification, object recognition labeled entities separately. For example, with image classification, the image is labeled as day or night. Object recognition individually tags various entities in an image, such as a bicycle, tree, table.
    • Segmentation: A more advanced form of image annotation. In order to analyze the image more easily, it divides the image into multiple segments and these parts are called image objects. There are three types of image segmentation:
      • Semantic segmentation: Label similar objects in the image according to their properties, such as their size and location.
      • Instance segmentation: Each entity in the image can be labeled. It defines the properties of entities such as position and number.
      • Panoptic segmentation: Both semantic and instance segmentations are used by combining.
  1. Text categorization: Text categorization assigns categories to the sentences in the document or the whole paragraph in accordance with the subject. Users can easily find the information they are looking for on the website.

What is the difference between data annotation and data labeling?

Both generate datasets to train machine learning models. However, there are a few minor differences:

Data AnnotationData Labeling
Label data to make objects identifiable by machinesAdd more info to different types of data such as text, video, and audio
Annotated data is required to train ML modelsLabeling is used to identify relevant features in the data
Annotation helps recognize relevant dataLabeling helps to train advanced algorithms to recognize patterns

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